CiliAI: Automated segmentation and compartment specific fluorescence quantification of primary cilia in confocal microscopy images

Primary cilia regulate essential signaling pathways controlling cell proliferation, differentiation, and tissue homeostasis. Quantitative analysis of ciliary morphology and compartment-specific protein localization by confocal microscopy is labor-intensive, user-dependent, and difficult to scale, particularly for multiplexed 3D image datasets. Here, we present CiliAI, a web-based deep-learning workflow for automated detection, substructure segmentation, and quantitative analysis of primary cilia in confocal microscopy images. CiliAI identifies ciliary substructures including the basal body, transition zone, and axoneme from multiplexed 3D image stacks and performs automated measurements of cilium length and compartment-specific fluorescence intensity. In NIH-3T3 cells, automated cilium length measurements showed close agreement with manual quantification and no statistically significant difference between methods (mean difference -0.214 {micro}m, p = 0.213). Automated fluorescence analysis reproduced previously reported reductions in transition zone-associated Cep290 signal intensity in Rpgrip1l-deficient cells and identified the absence of significant Rpgrip1l accumulation changes in Rmnd5a-deficient cells. Automated processing reduced analysis time from days of manual quantification to minutes. Together, these findings establish CiliAI as an automated framework for quantitative analysis of ciliary morphology and compartment-specific protein abundance in confocal microscopy datasets.